Regression models for choice-based samples with misclassification in the response variable

In this paper, we provide a general framework to deal with the presence of misclassification in the response variable in choice-based samples. The contaminated data sampling distribution is written as a function of the error-free conditional distribution of the dependent variable given the covariates and the conditional misclassification probabilities of the observable variable of interest given its latent values. We propose an extension of Imbens’ (Econometrica 60 (1992) 1187) efficient generalized method of moments to estimate this model and outline a specification test to detect the presence of this sort of measurement error. The performance of both the estimators and the test is investigated in a Monte Carlo simulation study, which shows very encouraging results.